Stock Market Perfomance Analysis

Stock Market Performance Analysis entails the computation of moving averages, assessment of volatility, correlation analysis, and exploration of diverse facets of the stock market.
Its aim is to acquire comprehensive insights into the elements that influence stock prices and the interdependencies among stock prices of distinct companies.
This analytical process assists in comprehending the dynamics of the stock market and aids in making informed investment decisions.

Let us import the necessary Python Libraries for the Analysis of the Stock Market Performance

In [1]:
import pandas as pd
import yfinance as yf
from datetime import datetime

start_date = datetime.now() - pd.DateOffset(months=3)
end_date = datetime.now()

Now we will search and download for stock prices from few tech companies, I have taken stocks from Apple, Micorsoft, Meta and Google from the last 3 months.

In [2]:
tickers = ['AAPL', 'MSFT', 'META', 'GOOG']
df_list = []
for ticker in tickers:
    data = yf.download(ticker, start = start_date, end = end_date)
    df_list.append(data)
    
df = pd.concat(df_list, keys=tickers, names=['Ticker', 'Date'])
print(df.head())

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                         Open        High         Low       Close   Adj Close  \
Ticker Date                                                                     
AAPL   2023-03-13  147.809998  153.139999  147.699997  150.470001  150.262161   
       2023-03-14  151.279999  153.399994  150.100006  152.589996  152.379227   
       2023-03-15  151.190002  153.250000  149.919998  152.990005  152.778687   
       2023-03-16  152.160004  156.460007  151.639999  155.850006  155.634735   
       2023-03-17  156.080002  156.740005  154.279999  155.000000  154.785904   

                     Volume  
Ticker Date                  
AAPL   2023-03-13  84457100  
       2023-03-14  73695900  
       2023-03-15  77167900  
       2023-03-16  76161100  
       2023-03-17  98944600

In the Dataset, we will set the Date Column as the Index Column

In [3]:
df = df.reset_index()
print(df.head())

  Ticker       Date        Open        High         Low       Close  \
0   AAPL 2023-03-13  147.809998  153.139999  147.699997  150.470001   
1   AAPL 2023-03-14  151.279999  153.399994  150.100006  152.589996   
2   AAPL 2023-03-15  151.190002  153.250000  149.919998  152.990005   
3   AAPL 2023-03-16  152.160004  156.460007  151.639999  155.850006   
4   AAPL 2023-03-17  156.080002  156.740005  154.279999  155.000000   

    Adj Close    Volume  
0  150.262161  84457100  
1  152.379227  73695900  
2  152.778687  77167900  
3  155.634735  76161100  
4  154.785904  98944600

Now Let's look at the perfomance of the companies in the stock market:

In [4]:
import plotly.express as px
fig = px.line(df, x='Date',
             y='Close',
             color='Ticker',
             title="Stock Market Porfomance from the last 3 Months")
fig.show()

Now Lets use the Area Chart and compare the perfomances of different Companies and Identify similarities or differences in their stock price movements

In [5]:
fig = px.area(df, x='Date', y='Close', color='Ticker', facet_col='Ticker',
             labels={'Date':'Date', 'Close':'Closing Price', 'Ticker':'Company'},
             title='Stock Prices of Apple, Micorsoft, Meta and Google')
fig.show()

Moving Averages

Now we'll analyze the moving averages, it'll provide a useful way to identify trends and patterns in each company's stock price movements over a certain period of time:

In [6]:
df['MA10'] = df.groupby('Ticker')['Close'].rolling(window=10).mean().reset_index(0, drop=True)
df['MA20'] = df.groupby('Ticker')['Close'].rolling(window=20).mean().reset_index(0, drop=True)

# Table View
#for ticker, group in df.groupby('Ticker'):
#    print(f'Moving Averages for {ticker}')
#    print(group[['MA10', 'MA20']])

#Visualization of the Moving Average
for ticker, group in df.groupby('Ticker'):
    fig = px.line(group, x = 'Date', 
                  y = ['Close', 'MA10', 'MA20'],
                 title = f"{ticker} Moving Average")
    fig.show()

The Above output we can observe that, When the MA10 crosses above MA20, it is considered a bullish signal indicating that the stock price will continue to rise. Conversly, when the MA10 crosses below the MA20, it is a bearish signal that the stock price will continue to fall.

Volatility

It measures how much and how often the stock price or market fluctuates over a given period of time.
High Volatility indicates that the stock or market experience are large and have frequent price movements, While low volatility indicates that the market experiences smaller or less frequent price movements.
Below we have visualization of volatility of all companies:

In [7]:
df['Volatility'] = df.groupby('Ticker')['Close'].pct_change().rolling(window=10).std().reset_index(0, drop=True)
fig = px.line(df, x = 'Date',
             y ='Volatility',
             color ='Ticker',
             title ='Volatility of All Companies')
fig.show()

Correlation

Let's Analyze the correlation between the stock prices of Meta and Google

In [8]:
#Create DataFrame with the Stock Pirces of the compnies corrlation you want to check
#For this Example, I took Meta and Google
meta = df.loc[df['Ticker'] == 'META', ['Date', 'Close']].rename(columns={'Close':'META'})
google = df.loc[df['Ticker'] == 'GOOG',['Date', 'Close']].rename(columns={'Close':'GOOG'})
df_corr = pd.merge(meta, google, on='Date')

#Visualization
fig = px.scatter(df_corr, x='META',
               y='GOOG',
                trendline='ols',
                title='Correlation between Meta and Google')
fig.show()

The stock prices of Meta and Google exhibit a significant positive linear relationship, implying that when Meta's stock price rises, Google's stock price tends to rise as well.
This correlation suggests similarities between the companies, possibly influenced by industry trends, market conditions, or shared business associations.
Investors can consider this correlation as an opportunity to diversify their portfolio, as both stocks offer comparable potential returns and risks.